diff --git a/doc/imgs/overview.png b/doc/imgs/overview.png index 83341cb3b96a257117f07e452993911277823f80..1c98dd47ed935781c56890c58a6819543d45de24 100644 Binary files a/doc/imgs/overview.png and b/doc/imgs/overview.png differ diff --git a/models/rank/fibinet/README.md b/models/rank/fibinet/README.md index 9e1eef9010a6d07d5738ecfee9ab4c5cf890a0dd..ea8c58d5b2eb6ef1208cf136d6ae516add42524f 100644 --- a/models/rank/fibinet/README.md +++ b/models/rank/fibinet/README.md @@ -30,6 +30,12 @@ （2）数值特征（连续特征）进行归一化处理 +执行run.sh生成训练集和测试集 + +``` +sh run.sh +``` + ## 环境 PaddlePaddle 1.7.2 @@ -97,38 +103,36 @@ python -m paddlerec.run -m paddlerec.models.rank.fibinet 训练： ``` -I0622 19:25:12.142271 344 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel. -I0622 19:25:12.673106 344 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1 -I0622 19:25:17.203287 344 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True -I0622 19:25:17.684131 344 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0 -batch: 10, AUC: [0.52777778], BATCH_AUC: [0.52777778] -batch: 20, AUC: [0.51836735], BATCH_AUC: [0.45098039] -batch: 30, AUC: [0.30978261], BATCH_AUC: [0.23214286] -epoch 0 done, use time: 11.074166536331177 -batch: 10, AUC: [0.44592593], BATCH_AUC: [0.74294671] -batch: 20, AUC: [0.52282609], BATCH_AUC: [0.83333333] -batch: 30, AUC: [0.5210356], BATCH_AUC: [0.91071429] -epoch 1 done, use time: 4.212069749832153 -batch: 10, AUC: [0.60075758], BATCH_AUC: [0.89184953] -batch: 20, AUC: [0.64758769], BATCH_AUC: [1.] -batch: 30, AUC: [0.68684476], BATCH_AUC: [1.] -epoch 2 done, use time: 4.276938438415527 -batch: 10, AUC: [0.75172139], BATCH_AUC: [1.] -batch: 20, AUC: [0.77915815], BATCH_AUC: [1.] -batch: 30, AUC: [0.81179181], BATCH_AUC: [1.] -epoch 3 done, use time: 4.278341770172119 -PaddleRec Finish +Running SingleStartup. +W0623 12:03:35.130075 509 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0 +W0623 12:03:35.134771 509 device_context.cc:245] device: 0, cuDNN Version: 7.3. +Running SingleRunner. +batch: 100, AUC: [0.6449976], BATCH_AUC: [0.69029814] +batch: 200, AUC: [0.6769844], BATCH_AUC: [0.70255003] +batch: 300, AUC: [0.67131597], BATCH_AUC: [0.68954499] +batch: 400, AUC: [0.68129822], BATCH_AUC: [0.70892718] +batch: 500, AUC: [0.68242937], BATCH_AUC: [0.69269376] +batch: 600, AUC: [0.68741928], BATCH_AUC: [0.72034578] +... +batch: 1400, AUC: [0.84607023], BATCH_AUC: [0.93358024] +batch: 1500, AUC: [0.84796116], BATCH_AUC: [0.95302841] +batch: 1600, AUC: [0.84949111], BATCH_AUC: [0.92868531] +batch: 1700, AUC: [0.85113661], BATCH_AUC: [0.95452616] +batch: 1800, AUC: [0.85260467], BATCH_AUC: [0.92847032] +epoch 3 done, use time: 1618.1106688976288 ``` 预测 ``` load persistables from increment_model/3 -batch: 20, AUC: [0.86578715], BATCH_AUC: [1.] -Infer phase2 of 3 done, use time: 13.813123941421509 -load persistables from increment_model/1 -batch: 20, AUC: [0.6480309], BATCH_AUC: [1.] -Infer phase2 of 1 done, use time: 13.001627922058105 -PaddleRec Finish +batch: 20, AUC: [0.85304064], BATCH_AUC: [0.94178556] +batch: 40, AUC: [0.85304544], BATCH_AUC: [0.95207907] +batch: 60, AUC: [0.85303907], BATCH_AUC: [0.94782551] +batch: 80, AUC: [0.85298773], BATCH_AUC: [0.93987691] +... +batch: 1780, AUC: [0.866046], BATCH_AUC: [0.96424594] +batch: 1800, AUC: [0.86633785], BATCH_AUC: [0.96900967] +batch: 1820, AUC: [0.86662365], BATCH_AUC: [0.96759972] ``` diff --git a/models/rank/fibinet/config.yaml b/models/rank/fibinet/config.yaml index 06dbc745493e00591bc8302f62911ff26f3f80be..520d709d16cbd24fe619e568d81e93f289328815 100644 --- a/models/rank/fibinet/config.yaml +++ b/models/rank/fibinet/config.yaml @@ -18,21 +18,15 @@ workspace: "paddlerec.models.rank.fibinet" # list of dataset dataset: - name: dataloader_train # name of dataset to distinguish different datasets - batch_size: 2 + batch_size: 1000 type: DataLoader # or QueueDataset - data_path: "{workspace}/data/sample_data/train" - sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26" - dense_slots: "dense_var:13" -- name: dataset_train # name of dataset to distinguish different datasets - batch_size: 2 - type: QueueDataset # or DataLoader - data_path: "{workspace}/data/sample_data/train" + data_path: "{workspace}/data/slot_test_data_full" sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26" dense_slots: "dense_var:13" - name: dataset_infer # name - batch_size: 2 + batch_size: 1000 type: DataLoader # or QueueDataset - data_path: "{workspace}/data/sample_data/train" + data_path: "{workspace}/data/slot_test_data_full" sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26" dense_slots: "dense_var:13" @@ -53,7 +47,7 @@ hyper_parameters: dropout_rate: 0.5 # select runner by name -mode: [single_cpu_train, single_cpu_infer] +mode: [single_gpu_train, single_gpu_infer] # config of each runner. # runner is a kind of paddle training class, which wraps the train/infer process. runner: @@ -63,23 +57,44 @@ runner: epochs: 4 # device to run training or infer device: cpu - save_checkpoint_interval: 2 # save model interval of epochs + save_checkpoint_interval: 1 # save model interval of epochs save_inference_interval: 4 # save inference save_checkpoint_path: "increment_model" # save checkpoint path save_inference_path: "inference" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path - print_interval: 10 + print_interval: 100 + phases: [phase1] + +- name: single_gpu_train + class: train + # num of epochs + epochs: 4 + # device to run training or infer + device: gpu + save_checkpoint_interval: 1 # save model interval of epochs + save_inference_interval: 4 # save inference + save_checkpoint_path: "increment_model" # save checkpoint path + save_inference_path: "inference" # save inference path + save_inference_feed_varnames: [] # feed vars of save inference + save_inference_fetch_varnames: [] # fetch vars of save inference + init_model_path: "" # load model path + print_interval: 100 phases: [phase1] - name: single_cpu_infer class: infer - # num of epochs - epochs: 1 # device to run training or infer device: cpu - init_model_path: "increment_model" # load model path + init_model_path: "increment_model/3" # load model path + phases: [phase2] + +- name: single_gpu_infer + class: infer + # device to run training or infer + device: gpu + init_model_path: "increment_model/3" # load model path phases: [phase2] # runner will run all the phase in each epoch @@ -87,10 +102,10 @@ phase: - name: phase1 model: "{workspace}/model.py" # user-defined model dataset_name: dataloader_train # select dataset by name - thread_num: 1 + thread_num: 8 - name: phase2 model: "{workspace}/model.py" # user-defined model dataset_name: dataset_infer # select dataset by name - thread_num: 1 + thread_num: 8 diff --git a/models/rank/readme.md b/models/rank/readme.md index 3ca96de21b7e134270adc448107c9db59f3632c3..b1939c9d3776eab86b48589698baa516130e9f60 100644 --- a/models/rank/readme.md +++ b/models/rank/readme.md @@ -37,7 +37,7 @@ | xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) | | DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) | | FGCNN | Feature Generation by CNN | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)(2019) | -| FIBINET | Combining Feature Importance and Bilinear feature Interaction | [《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf) | +| FIBINET | Combining Feature Importance and Bilinear feature Interaction | [《FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction》]( https://arxiv.org/pdf/1905.09433.pdf)(2019) | 下面是每个模型的简介（注：图片引用自链接中的论文） @@ -85,6 +85,7 @@ | DIN | 32 | 10 | 100 | | Wide&Deep | 40 | 1 | 40 | | xDeepFM | 100 | 1 | 10 | +| Fibinet | 1000 | 8 | 4 | ### 数据处理 参考每个模型目录数据下载&预处理脚本 @@ -124,6 +125,7 @@ python -m paddlerec.run -m ./config.yaml # 以DNN为例 | Criteo | xDeepFM | 0.48657 | -- | -- | -- | | Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- | | Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- | +| Criteo | Fibinet | -- | 0.86662 | -- | -- | ## 分布式